Gradient descent is an optimization algorithm that is used to train machine learning models and is now used in a neural network. Training data helps the model learn over time as gradient descent act as an automatic system that tunes parameters to achieve better results. These parameters are updated after each iteration until the function achieves the smallest possible error. The red arrow in the figure below is a gradient and by updating our parameters after each iteration we can reduce loss which is our primary goal. According to Arthur Samuel, gradient descent is the automatic processing of altering weights to maximize performance Fast AI.

The terms artificial intelligence, machine learning, and deep learning can be very unclear and muzzy sometimes even by its practitioners as it is used in the same context interchangeably. Lets begin with the popular "Artificial Intelligence". The term "Artificial Intelligence" has been around for over 60 years. Of course it triggers a lot of connotations to people when they hear the term especially at the first instance. Some of the ideas describes computers as being smart from probably getting some good article or data from the internet like Wikipedia.

Created in 1995 by the Engineering Faculty of the University of Mons, the research innovation center of Multitel has adopted IBM Watson Machine Learning Accelerator to harness the power of deep learning (DL) and tackle some of the biggest challenges of our time. Jean-Yves Parfait, AI Team Leader at Multitel and Franz Bourlet, Power Systems Expert at IBM, explain why ML is an added-value for industrial players. F. Bourlet, IBM: Arthur Samuel is one of the pioneers of machine learning. While working at IBM, Arthur Samuel wrote a Checker's playing program on IBM's first commercial computer 701. IBM Research has been exploring artificial intelligence and machine learning technologies and techniques for decades.

Artificial intelligence (AI) and machine learning (ML) have already changed the way we interact with each other and devices, and are poised to continually enhance markets, industries, and businesses globally. With ML and AI being used more rapidly, early adopters are experiencing significant competitive advantages. Arthur Samuel, an AI pioneer and the inventor of the first computerized checkers game, had a startling epiphany in 1959 that ultimately made today's algorithms possible. Up until then, most computer scientists assumed humans had to teach computers everything. Arthur Samuel realized computers could learn autonomously and coined the term "machine learning" (ML).

When you think of AI or machine learning you may draw up images of AlphaZero or even some science fiction reference such as HAL-9000 from 2001: A Space Odyssey. However, the true forefather, who set the stage for all of this, was the great Arthur Samuel. Samuel was a computer scientist, visionary, and pioneer, who wrote the first checkers program for the IBM 701 in the early 1950s. His program, "Samuel's Checkers Program", was first shown to the general public on TV on February 24th, 1956, and the impact was so powerful that IBM stock went up 15 points overnight (a huge jump at that time). This program also helped set the stage for all the modern chess programs we have come to know so well, with features like look-ahead, an evaluation function, and a mini-max search that he would later develop into alpha-beta pruning.

This big data discipline of artificial intelligence gives systems the freedom to automatically gain information and improve from experience without manual programming. Machine learning is literally just that – "letting the machine learn". The definition of machine learning is "the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model of sample data, known as'training data', in order to make predictions or decisions without being explicitly programmed to perform the task".

Programmed by Arthur Samuel, this big data discipline of artificial intelligence replaces the tedious task of trying to understand the problem well enough to be able to write a program, which can take much longer or be virtually impossible. Techopedia defines the discipline of machine learning as "an artificial intelligence (AI) discipline geared toward the technological development of human knowledge. Machine learning allows computers to handle new situations via analysis, self-training, observation and experience. Machine learning facilitates the continuous advancement of computing through exposure to new scenarios, testing and adaptation, while employing pattern and trend detection for improved decisions in subsequent (though not identical) situations." In 1959, IBM employee Arthur Samuel wanted to teach a computer to play checkers. So, he wrote the original program on IBM's first commercial computer, the IBM 701, but he kept winning.

Have you ever heard someone describe artificial intelligence as machine learning? Or have you ever heard someone describe machine learning as artificial intelligence? Maybe you've only heard one of these terms and are completely lost at this point. We've noticed people interchanging both of these terms, and we are here to clear the air. Artificial intelligence, or AI, was a term developed by John McCarthy in 1956.

Arthur Samuel, an eccentric computer engineer at Stanford University, took part in what could be considered the most important game of checkers ever played. Arthur challenged the then reigning Connecticut state champion to match wits with a computer he programmed to play checkers.a Surprisingly enough, this event is not an artifact of recent history; the fateful game took place in 1961. Decades prior to the personal computer revolution, Professor Samuel built a working prototype capable of what we now call, "machine learning." Rather than programming the 500 quintillion b potential scenarios on a checkerboard, Arthur instructed the computer to react based on games it had played in the past.

On your screens, in your pockets and one day may even be walking to a home near you. The headlines tend to group together this vast and diverse field into one subject. Robots emerging from the labs, algorithms playing ancient games and winning, AI and its promises are becoming a part of our everyday lives. While all of these instances have some relationship to AI, this is not a monolithic field, but one that has many separate and distinct disciplines. A lot of the times we use the term Artificial intelligence as an all-encompassing umbrella term that covers everything.